library(tidyverse)
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[30m-- [1mConflicts[22m ------------------------------------------ tidyverse_conflicts() --
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library(readxl)
library(lubridate)
Attaching package: <U+393C><U+3E31>lubridate<U+393C><U+3E32>
The following object is masked from <U+393C><U+3E31>package:base<U+393C><U+3E32>:
date
aid <- read_excel("Aid_received.xlsx", sheet = "Data")
mapping <- read_excel("CountryMapping.xlsx", sheet = "Countries_etc")
aid_gathered <- aid %>%
gather(key = 'year', value = 'aid', -`ODA received, total (constant 2010 US$)`)
country_aid <- inner_join(aid_gathered,
mapping, by = c('ODA received, total (constant 2010 US$)' = 'name'))
library(janitor)
country_aid_clean <- clean_names(country_aid)
aid_all <- country_aid_clean %>%
rename(country = oda_received_total_constant_2010_us)
aid_all$year <- str_c(aid_all$year, '01','01', sep = "-")
aid_all$year <- ymd(aid_all$year)
glimpse(aid_all)
Observations: 9,996
Variables: 13
$ country <chr> "Afghanistan", "Albania", "Algeria", "Andorra", "Angola", "Antigua and Barbuda", "Argentina", "Armeni...
$ year <date> 1960-01-01, 1960-01-01, 1960-01-01, 1960-01-01, 1960-01-01, 1960-01-01, 1960-01-01, 1960-01-01, 1960...
$ aid <dbl> 108800000, NA, 3405040000, NA, -390000, NA, 91670000, NA, NA, NA, NA, -390000, 750000, NA, NA, NA, NA...
$ geo <chr> "afg", "alb", "dza", "and", "ago", "atg", "arg", "arm", "aus", "aut", "aze", "bhs", "bhr", "bgd", "br...
$ gapminders_4_world_regions <chr> "asia", "europe", "africa", "europe", "africa", "americas", "americas", "europe", "asia", "europe", "...
$ gapminders_8_subregions <chr> "asia_west", "europe_east", "africa_north", "europe_west", "africa_sub_saharan", "america_north", "am...
$ gapminders_6_world_regions <chr> "south_asia", "europe_central_asia", "middle_east_north_africa", "europe_central_asia", "sub_saharan_...
$ members_of_g77_or_oecd <chr> "g77", "others", "g77", "others", "g77", "g77", "g77", "others", "oecd", "oecd", "others", "g77", "g7...
$ latitude <dbl> 33.00000, 41.00000, 28.00000, 42.50779, -12.50000, 17.05000, -34.00000, 40.25000, -25.00000, 47.33333...
$ longitude <dbl> 66.00000, 20.00000, 3.00000, 1.52109, 18.50000, -61.80000, -64.00000, 45.00000, 135.00000, 13.33333, ...
$ un_member_sinces <dttm> 1946-11-19, 1955-12-14, 1962-10-08, 1993-07-28, 1976-12-01, 1981-11-11, 1945-10-24, 1992-03-02, 1945...
$ world_bank_region <chr> "South Asia", "Europe & Central Asia", "Middle East & North Africa", "Europe & Central Asia", "Sub-Sa...
$ world_bank_income_group_2017 <chr> "Low income", "Upper middle income", "Upper middle income", "High income", "Lower middle income", "Hi...
class_plot<-ggplot(aid_all, aes(y=aid, x=year))
class_plot+geom_point()

class_plot+geom_point(aes(color=gapminders_4_world_regions), alpha=.5, size=1)

class_plot+
geom_point(aes(color=gapminders_4_world_regions), alpha=.5, size=1)+
coord_cartesian(ylim = c(0,10000000000))

class_plot+
geom_point(aes(color=gapminders_4_world_regions), alpha=.5, size=1)+
ylim(0,10000000000)

class_plot+
geom_point(aes(color=gapminders_4_world_regions), alpha=.5, size=1)+
facet_wrap(~gapminders_8_subregions)+
scale_color_discrete(name="World Bank Income Group 2017")

aid_2010 <-
country_aid_clean %>%
filter(year == 2010)
class_hist<-ggplot(aid_2010, aes(aid))
class_hist+geom_histogram()

class_hist2<-ggplot(aid_2010, aes(log(aid)))
class_hist2+geom_histogram(binwidth=1)

class_hist+geom_histogram(aes(fill=gapminders_4_world_regions))

class_hist+geom_density(aes(color=gapminders_4_world_regions))

class_hist+
geom_col(aes(y=aid, x=gapminders_4_world_regions, fill=world_bank_income_group_2017),position = "stack")

library(RColorBrewer)
class_hist+geom_boxplot(aes(y=aid, x=gapminders_4_world_regions, fill=gapminders_4_world_regions))+
ggtitle("Aid 2010", " by Gapminder's 4 World Regions")+
xlab("Gapminder's 4 World Regions") +
ylab("Aid") +
scale_fill_brewer(palette="Dark2")

class_hist+geom_boxplot(aes(y=aid, x=gapminders_4_world_regions, fill=gapminders_4_world_regions))+
ggtitle("Aid 2010", " by Gapminder's 4 World Regions")+
xlab("Gapminder's 4 World Regions") +
ylab("Aid") +
scale_fill_brewer(palette="Dark2")+
geom_hline(yintercept=med, linetype=3)
Error in data.frame(yintercept = yintercept) : object 'med' not found
top_variance <- aid_all %>%
group_by(country) %>%
mutate(variance = var(aid, na.rm = TRUE)) %>%
arrange(desc(variance)) %>%
distinct(country, variance) %>%
ungroup() %>%
slice(1:10)
top_variance <- aid_all %>%
filter(country %in% top_variance$country)
top_variance

plot_ly(aid_subset, x = ~year, y = ~aid, type = "scatter",color = ~factor(country))
No scatter mode specifed:
Setting the mode to markers
Read more about this attribute -> https://plot.ly/r/reference/#scatter-mode
n too large, allowed maximum for palette Set2 is 8
Returning the palette you asked for with that many colors
No scatter mode specifed:
Setting the mode to markers
Read more about this attribute -> https://plot.ly/r/reference/#scatter-mode
n too large, allowed maximum for palette Set2 is 8
Returning the palette you asked for with that many colors
dmddata_sub<-as.data.frame(subset(dmddata, select=c("carat","cut","color","clarity", "price"))) %>%
group_by(carat, cut, color, clarity) %>%
summarise_each(funs(mean))
`summarise_each()` is deprecated.
Use `summarise_all()`, `summarise_at()` or `summarise_if()` instead.
To map `funs` over all variables, use `summarise_all()`
dmddata_sub20+
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DQoNCg==